2P1-S06 Development of preceding vehicle recognition algorithm for autonomous driving and platooning : Preceding vehicle recognition algorithm based on road curvature estimation(ITS and Robot Technology)

2012 ◽  
Vol 2012 (0) ◽  
pp. _2P1-S06_1-_2P1-S06_4
Author(s):  
Shota KOIKE ◽  
Naoki SUGANUMA
Keyword(s):  
Autonomous Driving ◽  
Recognition Algorithm ◽  
Vehicle Recognition ◽  
Preceding Vehicle ◽  
Curvature Estimation ◽  
Robot Technology

scholarly journals Intelligent Highway Traffic Detection Algorithm based on Deep Learning

2021 ◽  
Author(s):  
ming ji ◽  
Chuanxia Sun ◽  
Yinglei Hu
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Traffic Congestion ◽  
Road Network ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Traffic Data ◽  
Vehicle Recognition ◽  
Environment Recognition

Abstract In order to solve the increasingly serious traffic congestion problem, an intelligent transportation system is widely used in dynamic traffic management, which effectively alleviates traffic congestion and improves road traffic efficiency. With the continuous development of traffic data acquisition technology, it is possible to obtain real-time traffic data in the road network in time. A large amount of traffic information provides a data guarantee for the analysis and prediction of road network traffic state. Based on the deep learning framework, this paper studies the vehicle recognition algorithm and road environment discrimination algorithm, which greatly improves the accuracy of highway vehicle recognition. Collect highway video surveillance images in different environments, establish a complete original database, build a deep learning model of environment discrimination, and train the classification model to realize real-time environment recognition of highway, as the basic condition of vehicle recognition and traffic event discrimination, and provide basic information for vehicle detection model selection. To improve the accuracy of road vehicle detection, the vehicle target labeling and sample preprocessing of different environment samples are carried out. On this basis, the vehicle recognition algorithm is studied, and the vehicle detection algorithm based on weather environment recognition and fast RCNN model is proposed. Then, the performance of the vehicle detection algorithm described in this paper is verified by comparing the detection accuracy differences between different environment dataset models and overall dataset models, different network structures and deep learning methods, and other methods.

A Parallelism Analysis Pattern for Real-time Vehicle Recognition Algorithm

2007 ◽  
Author(s):  
Chunyang Yang ◽  
Huai Yuan ◽  
Nan Wang ◽  
Chunmin Zhang ◽  
Bobo Duan
Keyword(s):  
Real Time ◽  
Recognition Algorithm ◽  
Vehicle Recognition ◽  
Analysis Pattern

AlTwo: Vehicle Recognition in Foggy Weather Based on Two-Step Recognition Algorithm

2020 ◽  
pp. 130-141
Author(s):  
Fengxin Li ◽  
Ziye Luo ◽  
Jingyu Huang ◽  
Lingzhan Wang ◽  
Jinpu Cai ◽  
...  
Keyword(s):  
Recognition Algorithm ◽  
Vehicle Recognition

A Study of Self-Adaptive Front Vehicle Recognition Algorithm Based on Multi-Features

2014 ◽  
Vol 945-949 ◽  
pp. 1815-1819
Author(s):  
Mei Hua Xu ◽  
Chen Jun Xia ◽  
Huai Meng Zheng
Keyword(s):  
Vehicle Detection ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
License Plate ◽  
Improved Method ◽  
Vehicle Recognition ◽  
Potential Risks ◽  
Self Adaptive

With the development of intelligent driving technology, recognition the vehicle in front of our cars became the hotspot in the field of intelligent driving research. This paper presents a self-adaptive front vehicle recognition algorithm with some unique improved method on the basis of analyzing and comparing the popular vehicle detection algorithm of domestic and foreign. Using the gray feature, vehicle shadow feature, taillights feature, license plate color domain feature and other features, the recognition algorithm can detect the vehicle in front of cars effectively, find out the safe passage area and avoid the potential risks. Finally, the feasibility of the algorithm is verified by experiment results with MATLAB tools.

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